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Poster communications

Object Categorization Using Boosting Within Hierarchical Bayesian Model

Yi Ji 1 Khalid Idrissi 1 Atilla Baskurt 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper we address the problem of generative object categorization in computer vision. We propose a Bayesian model using Hierarchical Dirichlet Processes mixing AdaBoost learning. Although previous methods trained HDP model for one or two latent themes, our proposed approach uses small-patch-independent-words of appearance-based descriptor and shape information to train a set of intermediate components which are the mixture of visualwords. We then employ AdaBoost weaker learner to find the most related components for classification to handle the variance in intraclass and inter-class information. We show that it performs well for Caltech datasets and with the potential to connect the visual concepts with semantic concepts.
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Submitted on : Tuesday, January 17, 2017 - 1:58:48 PM
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Yi Ji, Khalid Idrissi, Atilla Baskurt. Object Categorization Using Boosting Within Hierarchical Bayesian Model. Int. Conf. on Image Processing, Nov 2009, Le Caire, Egypt. IEEE, pp.317-320, 2009, ⟨10.1109/ICIP.2009.5414507⟩. ⟨hal-01437770⟩



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